Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "163" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 27 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 27 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460010 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.616205 | 1.630455 | -0.133157 | 0.601735 | 0.370280 | 2.327465 | -0.209012 | 0.981277 | 0.6440 | 0.6606 | 0.3482 | nan | nan |
| 2460009 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.697956 | 1.835869 | 0.123257 | 0.796706 | 0.243623 | 1.292365 | -0.321112 | 0.759774 | 0.6430 | 0.6594 | 0.3536 | nan | nan |
| 2460008 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.203269 | 2.698651 | -0.026733 | 0.794580 | -0.131027 | 1.595517 | 0.036697 | 1.186483 | 0.6839 | 0.6991 | 0.3110 | nan | nan |
| 2460007 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.727579 | 1.525139 | 0.140363 | 0.793632 | 0.379188 | 1.810617 | 0.281155 | 1.297281 | 0.6507 | 0.6666 | 0.3370 | nan | nan |
| 2459999 | digital_ok | 0.00% | 98.58% | 98.91% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.3275 | 0.2440 | 0.2889 | nan | nan |
| 2459998 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.681987 | 1.478350 | 0.107335 | 0.581997 | 0.491084 | 1.723142 | -0.145480 | 1.647673 | 0.6310 | 0.6474 | 0.3780 | nan | nan |
| 2459997 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459996 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.518212 | 1.746419 | 0.522292 | 0.982999 | 0.423599 | 1.687179 | -0.104174 | 0.525237 | 0.6482 | 0.6609 | 0.3909 | nan | nan |
| 2459995 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.376244 | 1.724273 | -0.005271 | 0.595800 | 0.000071 | 1.493011 | -0.372243 | 1.148559 | 0.6399 | 0.6565 | 0.3811 | nan | nan |
| 2459994 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.815116 | 1.745342 | 0.009335 | 0.721840 | 0.157135 | 1.239736 | -0.190218 | 1.252235 | 0.6356 | 0.6509 | 0.3778 | nan | nan |
| 2459993 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.340239 | 1.564094 | -0.228757 | 0.564289 | -0.344436 | 1.050554 | -0.456527 | 1.298530 | 0.6219 | 0.6507 | 0.3901 | nan | nan |
| 2459991 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.554636 | 1.698968 | -0.080166 | 0.596232 | -0.227285 | 1.163955 | -0.370055 | 0.934461 | 0.6454 | 0.6534 | 0.3822 | nan | nan |
| 2459990 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.826531 | 1.403730 | -0.057774 | 0.542904 | -0.000810 | 1.287768 | -0.503237 | 0.917307 | 0.6423 | 0.6529 | 0.3794 | nan | nan |
| 2459989 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.019857 | 1.313774 | -0.065191 | 0.728043 | 0.000012 | 1.055390 | -0.308646 | 0.950544 | 0.6347 | 0.6495 | 0.3834 | nan | nan |
| 2459988 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.161960 | 1.819705 | -0.131793 | 0.459986 | 0.077916 | 0.740476 | -0.248718 | 1.032655 | 0.6387 | 0.6542 | 0.3751 | nan | nan |
| 2459987 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.059380 | 1.410950 | -0.102442 | 0.625303 | 0.123833 | 0.756921 | 0.278780 | 2.310549 | 0.6459 | 0.6572 | 0.3707 | nan | nan |
| 2459986 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.080690 | 2.016130 | -0.091376 | 0.536896 | -0.019939 | 0.611955 | -0.135872 | 1.074072 | 0.6664 | 0.6815 | 0.3290 | nan | nan |
| 2459985 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.076425 | 1.949896 | -0.076327 | 0.566678 | -0.019026 | 1.232021 | -0.090864 | 2.485038 | 0.6475 | 0.6574 | 0.3794 | nan | nan |
| 2459984 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.307294 | 1.737404 | -0.136724 | 0.524166 | 2.250583 | 1.858010 | 0.108319 | 0.221877 | 0.6618 | 0.6731 | 0.3559 | nan | nan |
| 2459983 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.648572 | 1.373327 | -0.056469 | 0.524285 | -0.115337 | 0.784301 | -0.324911 | 0.679704 | 0.6731 | 0.6928 | 0.3132 | nan | nan |
| 2459982 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.978911 | 1.315164 | 0.092374 | 0.629022 | 0.367790 | 0.686710 | -0.006296 | 0.552159 | 0.7253 | 0.7262 | 0.2738 | nan | nan |
| 2459981 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.604503 | 0.995438 | -0.187089 | 0.465828 | -0.225410 | 0.605383 | -0.411603 | 0.798661 | 0.6474 | 0.6579 | 0.3747 | nan | nan |
| 2459980 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.875610 | 1.393812 | -0.249021 | 0.452176 | -0.124432 | 0.853965 | -0.144232 | 0.698951 | 0.6896 | 0.6975 | 0.2971 | nan | nan |
| 2459979 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.462743 | 0.904597 | -0.360251 | 0.456819 | -0.249137 | 0.202649 | -0.508792 | 0.518256 | 0.6392 | 0.6535 | 0.3775 | nan | nan |
| 2459978 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.537437 | 0.930985 | -0.339553 | 0.430539 | -0.111049 | 0.490983 | -0.314783 | 1.327284 | 0.6398 | 0.6521 | 0.3831 | nan | nan |
| 2459977 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.481467 | 1.141244 | -0.281740 | 0.433216 | -0.200396 | 1.140363 | 0.072550 | 1.507577 | 0.6061 | 0.6187 | 0.3461 | nan | nan |
| 2459976 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.475000 | 1.095572 | -0.262688 | 0.416162 | -0.406345 | 1.083600 | -0.374946 | 0.892958 | 0.6468 | 0.6584 | 0.3710 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Temporal Variability | 2.327465 | 0.616205 | 1.630455 | -0.133157 | 0.601735 | 0.370280 | 2.327465 | -0.209012 | 0.981277 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Shape | 1.835869 | 0.697956 | 1.835869 | 0.123257 | 0.796706 | 0.243623 | 1.292365 | -0.321112 | 0.759774 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Shape | 2.698651 | 2.698651 | 1.203269 | 0.794580 | -0.026733 | 1.595517 | -0.131027 | 1.186483 | 0.036697 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Temporal Variability | 1.810617 | 0.727579 | 1.525139 | 0.140363 | 0.793632 | 0.379188 | 1.810617 | 0.281155 | 1.297281 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Temporal Variability | 1.723142 | 0.681987 | 1.478350 | 0.107335 | 0.581997 | 0.491084 | 1.723142 | -0.145480 | 1.647673 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Shape | 1.746419 | 0.518212 | 1.746419 | 0.522292 | 0.982999 | 0.423599 | 1.687179 | -0.104174 | 0.525237 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Shape | 1.724273 | 0.376244 | 1.724273 | -0.005271 | 0.595800 | 0.000071 | 1.493011 | -0.372243 | 1.148559 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Shape | 1.745342 | 0.815116 | 1.745342 | 0.009335 | 0.721840 | 0.157135 | 1.239736 | -0.190218 | 1.252235 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Shape | 1.564094 | 0.340239 | 1.564094 | -0.228757 | 0.564289 | -0.344436 | 1.050554 | -0.456527 | 1.298530 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Shape | 1.698968 | 0.554636 | 1.698968 | -0.080166 | 0.596232 | -0.227285 | 1.163955 | -0.370055 | 0.934461 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Shape | 1.403730 | 1.403730 | 0.826531 | 0.542904 | -0.057774 | 1.287768 | -0.000810 | 0.917307 | -0.503237 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Shape | 1.313774 | 1.313774 | 1.019857 | 0.728043 | -0.065191 | 1.055390 | 0.000012 | 0.950544 | -0.308646 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Shape | 1.819705 | 1.819705 | 1.161960 | 0.459986 | -0.131793 | 0.740476 | 0.077916 | 1.032655 | -0.248718 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Temporal Discontinuties | 2.310549 | 1.059380 | 1.410950 | -0.102442 | 0.625303 | 0.123833 | 0.756921 | 0.278780 | 2.310549 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Shape | 2.016130 | 2.016130 | 1.080690 | 0.536896 | -0.091376 | 0.611955 | -0.019939 | 1.074072 | -0.135872 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Temporal Discontinuties | 2.485038 | 1.949896 | 1.076425 | 0.566678 | -0.076327 | 1.232021 | -0.019026 | 2.485038 | -0.090864 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | ee Temporal Variability | 2.250583 | 1.307294 | 1.737404 | -0.136724 | 0.524166 | 2.250583 | 1.858010 | 0.108319 | 0.221877 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Shape | 1.373327 | 0.648572 | 1.373327 | -0.056469 | 0.524285 | -0.115337 | 0.784301 | -0.324911 | 0.679704 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Shape | 1.315164 | 0.978911 | 1.315164 | 0.092374 | 0.629022 | 0.367790 | 0.686710 | -0.006296 | 0.552159 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Shape | 0.995438 | 0.995438 | 0.604503 | 0.465828 | -0.187089 | 0.605383 | -0.225410 | 0.798661 | -0.411603 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Shape | 1.393812 | 1.393812 | 0.875610 | 0.452176 | -0.249021 | 0.853965 | -0.124432 | 0.698951 | -0.144232 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Shape | 0.904597 | 0.462743 | 0.904597 | -0.360251 | 0.456819 | -0.249137 | 0.202649 | -0.508792 | 0.518256 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Temporal Discontinuties | 1.327284 | 0.930985 | 0.537437 | 0.430539 | -0.339553 | 0.490983 | -0.111049 | 1.327284 | -0.314783 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Temporal Discontinuties | 1.507577 | 0.481467 | 1.141244 | -0.281740 | 0.433216 | -0.200396 | 1.140363 | 0.072550 | 1.507577 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 163 | N14 | digital_ok | nn Shape | 1.095572 | 1.095572 | 0.475000 | 0.416162 | -0.262688 | 1.083600 | -0.406345 | 0.892958 | -0.374946 |